Trelliscope Project

1 Old Trelliscope

Follow the tutorial for the old version of trelliscope at this link

Old Version Tutorial

Code

library(trelliscopejs)
library(ggplot2)
library(gapminder)

str(gapminder)

tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
 $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
 $ year     : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
 $ lifeExp  : num [1:1704] 28.8 30.3 32 34 36.1 ...
 $ pop      : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
 $ gdpPercap: num [1:1704] 779 821 853 836 740 ...

Code

qplot(year, lifeExp, data = subset(gapminder, continent == "Europe")) +
  facet_wrap(~ country + continent) +
  theme_bw()

Code

qplot(year, lifeExp, data = gapminder) +
  xlim(1948, 2011) + ylim(10, 95) + theme_bw() +
  facet_wrap(~ country + continent)

Code

qplot(year, lifeExp, data = gapminder) +
  xlim(1948, 2011) + ylim(10, 95) + theme_bw() +
  facet_trelliscope(~ country + continent, nrow = 2, ncol = 7, width = 300, path = "nba1", self_contained = TRUE)

Code

library(dplyr)
library(tidyr)
library(purrr)
library(gapminder)

by_country <- nest(gapminder, data = !one_of(c("country", "continent")))
by_country

# A tibble: 142 × 3
   country     continent data             
   <fct>       <fct>     <list>           
 1 Afghanistan Asia      <tibble [12 × 4]>
 2 Albania     Europe    <tibble [12 × 4]>
 3 Algeria     Africa    <tibble [12 × 4]>
 4 Angola      Africa    <tibble [12 × 4]>
 5 Argentina   Americas  <tibble [12 × 4]>
 6 Australia   Oceania   <tibble [12 × 4]>
 7 Austria     Europe    <tibble [12 × 4]>
 8 Bahrain     Asia      <tibble [12 × 4]>
 9 Bangladesh  Asia      <tibble [12 × 4]>
10 Belgium     Europe    <tibble [12 × 4]>
# … with 132 more rows

Code

country_model <- function(df)
  lm(lifeExp ~ year, data = df)

by_country <- by_country %>%
  mutate(model = map(data, country_model))

by_country

# A tibble: 142 × 4
   country     continent data              model 
   <fct>       <fct>     <list>            <list>
 1 Afghanistan Asia      <tibble [12 × 4]> <lm>  
 2 Albania     Europe    <tibble [12 × 4]> <lm>  
 3 Algeria     Africa    <tibble [12 × 4]> <lm>  
 4 Angola      Africa    <tibble [12 × 4]> <lm>  
 5 Argentina   Americas  <tibble [12 × 4]> <lm>  
 6 Australia   Oceania   <tibble [12 × 4]> <lm>  
 7 Austria     Europe    <tibble [12 × 4]> <lm>  
 8 Bahrain     Asia      <tibble [12 × 4]> <lm>  
 9 Bangladesh  Asia      <tibble [12 × 4]> <lm>  
10 Belgium     Europe    <tibble [12 × 4]> <lm>  
# … with 132 more rows

Code

library(plotly)
library(trelliscopejs)

country_plot <- function(data, model) {
  plot_ly(data = data, x = ~year, y = ~lifeExp,
    type = "scatter", mode = "markers", name = "data") %>%
    add_trace(data = data, x = ~year, y = ~predict(model),
      mode = "lines", name = "lm") %>%
    layout(
      xaxis = list(range = c(1948, 2011)),
      yaxis = list(range = c(10, 95)),
      showlegend = FALSE)
}

by_country <- by_country %>%
  mutate(data_plot = map2_plot(data, model, country_plot))

by_country

# A tibble: 142 × 5
   country     continent data              model  data_plot 
   <fct>       <fct>     <list>            <list> <trllscp_>
 1 Afghanistan Asia      <tibble [12 × 4]> <lm>   <plotly>  
 2 Albania     Europe    <tibble [12 × 4]> <lm>   <plotly>  
 3 Algeria     Africa    <tibble [12 × 4]> <lm>   <plotly>  
 4 Angola      Africa    <tibble [12 × 4]> <lm>   <plotly>  
 5 Argentina   Americas  <tibble [12 × 4]> <lm>   <plotly>  
 6 Australia   Oceania   <tibble [12 × 4]> <lm>   <plotly>  
 7 Austria     Europe    <tibble [12 × 4]> <lm>   <plotly>  
 8 Bahrain     Asia      <tibble [12 × 4]> <lm>   <plotly>  
 9 Bangladesh  Asia      <tibble [12 × 4]> <lm>   <plotly>  
10 Belgium     Europe    <tibble [12 × 4]> <lm>   <plotly>  
# … with 132 more rows

Code

by_country %>%
  trelliscope(name = "life_expectancy", nrow = 2, ncol = 4, path = "nba1", self_contained = TRUE)

Code

by_country <- by_country %>%
  mutate(resid_mad = cog(
    map_dbl(model, ~ mad(resid(.x))),
    desc = "median absolute deviation of residuals"))

Code

by_country %>%
  arrange(-resid_mad) %>%
  trelliscope(name = "by_country_lm", nrow = 2, ncol = 4, path = "nba1", self_contained = TRUE)

Code

by_country %>%
  filter(continent == "Africa") %>%
  trelliscope(name = "by_country_africa_lm", nrow = 2, ncol = 4, path = "nba1", self_contained = TRUE)

Code

library(housingData)
housing$state <- as.character(housing$state)
housing$county <- as.character(housing$county)

slope_fn <- function(x, y)
  coef(lm(y ~ x))[2]

d <- nest(housing, data = !one_of(c("county", "state")))

d <- d %>%
  mutate(
    cogs = map(data, function(x, state, county) {
      tibble(
        slope = slope_fn(x$time, x$medListPriceSqft),
        mean_list = mean(x$medListPriceSqft, na.rm = TRUE),
        mean_sold = mean(x$medSoldPriceSqft, na.rm = TRUE),
        n_obs = length(which(!is.na(x$medListPriceSqft)))
      )
    }),
    zillow_link = sprintf("http://www.zillow.com/homes/%s_rb/",
      gsub(" ", "-", paste(county, state)))
  ) %>%
  unnest(cogs) %>%
  filter(n_obs > 1)

d <- d %>%
  mutate(
    panel = map_plot(data, function(x) {
      plot_ly(data = x, x = ~time, y = ~medListPriceSqft,
        type = "scatter", mode = "markers") %>%
        layout(
          xaxis = list(title = "time"),
          yaxis = list(title = "median list price / sq ft"))
    })
  )

d %>%
  trelliscope(name = "list_vs_time",
    desc = "monthly median list price vs. time for 2984 US counties from 2008–2016",
    nrow = 2, ncol = 4, path = "nba1", self_contained = TRUE)

2 New Trelliscope

Then install the new version of trelliscope from this repo and follow it’s readme tutorial. Note that the graphical interface at the end won’t render but the code should al work up to that point. Make sure you’re not getting any errors and make sure your final table has all the necessary plots and what not.

New Version Tutorial

3 Vignettes

Clone this repo to your local machine. You will open vignettes in this repo one by one and follow their tutorials with the intent to suggest improvements in two ways; generallly, how could the writing be improved and specifically what changes need to be made to update the code and writing to align with new trelliscope? You will do this with the following trelliscopes in this order: Host a Trelliscope on Github, password_protecting, related displays, preserving_state_local.

Vignettes Improvement

4 Vignettes Comments

Add comments and suggestions for edits to the issues thread here (note that this is an issue in the trelliscope-vignettes repo)

Comments about Vignettes on GitHub Issue

5 Package Comments

Additionally, add comments on how the new trelliscope package could be improved from a user standpoint here

Comments about Trelliscope Package Improvements

biggest confusion is about the path, temporary file path, and getting the plots to render in the output

6 NBA Trelliscope

Build a trelliscope with data from this repo. Make it pretty. Try implementing some of the skills you’ve learned from the tutorials. Specifially, host the final trelliscope on github, password protect it, and use cognostoc groups. This trelliscope needs to be functional so build it using the old trelliscope package.

NBA

Code

nba_dat1 <- nba_dat|>
  dplyr::filter(year_season <= 1990) |>
  dplyr::group_by(year_season, name_team, location_game) |>
  dplyr::summarise(point_sd = sd(pts))

# plot1 <- ggplot2::qplot(year_season, point_sd, data = nba_dat1)+ 
#   facet_trelliscope(~ name_team + location_game, 
#                     name = "NBA Stats1", 
#                     path = "trelliscopes", 
#                     nrow = 2, ncol = 4)
# 
# plot1


sd_points_graph <- nba_dat|>
  dplyr::filter(year_season <= 1990) |>
  dplyr::group_by(year_season, name_team, location_game) |>
  dplyr::summarise(point_sd = sd(pts)) |>
  ggplot2::ggplot()+
  ggplot2::geom_point(ggplot2::aes(year_season, point_sd))


sd1 <- sd_points_graph + 
  facet_trelliscope(~ name_team + location_game,
                    nrow = 2, ncol = 4,
                    name = "NBA Stats", path = "nba1")

sd1 |>
  tr_charm(password = "my_password")

Code

#  eval: TRUE

nba_dat2 <- nba_dat |>
  dplyr::filter(year_season <= 1990) |>
  dplyr::group_by(year_season, name_team, location_game) |>
  dplyr::summarise(point_mean = mean(pts))

plot2 <- ggplot2::qplot(year_season, point_mean, data = nba_dat2)+
  trelliscopejs::facet_trelliscope(~ name_team + location_game, 
                                   name = "NBA Stats2", path = "nba1",
                                   nrow = 2, ncol = 4)

plot2